We identify LSD1 (lysine-specific demethylase 1; also known as KDM1A and AOF2) as a key histone modifier that participates in the maintenance of pluripotency through the regulation of bivalent domains, a chromatin environment present at the regulatory regions of developmental genes that contains both H3K4 di/trimethylation and H3K27 trimethylation marks. LSD1 occupies the promoters of a subset of developmental genes that contain bivalent domains and are co-occupied by OCT4 and NANOG in human embryonic stem cells, where it controls the levels of H3K4 methylation through its demethylase activity. Thus, LSD1 has a role in maintaining the silencing of several developmental genes in human embryonic stem cells by regulating the critical balance between H3K4 and H3K27 methylation at their regulatory regions.

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Transcription-factor-induced reprogramming of somatic cells to pluripotency is a very inefficient process, probably due to the existence of important epigenetic barriers that are imposed during differentiation and that contribute to preserving cell identity. In an effort to decipher the molecular nature of these barriers, we followed a genome-wide approach, in which we identified macrohistone variants (macroH2A) as highly expressed in human somatic cells but downregulated after reprogramming to pluripotency, as well as strongly induced during differentiation. Knockdown of macrohistone variants in human keratinocytes increased the efficiency of reprogramming to pluripotency, whereas overexpression had opposite effects. Genome-wide occupancy profiles show that in human keratinocytes, macroH2A.1 preferentially occupies genes that are expressed at low levels and are marked with H3K27me3, including pluripotency-related genes and bivalent developmental regulators. The presence of macroH2A.1 at these genes prevents the regain of H3K4me2 during reprogramming, imposing an additional layer of repression that preserves cell identity.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes.

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